Registered StackHub users may elect to receive email notifications whenever a new package version is released.
There are 5 watchers.
Build the LBNL-4944E data model. This function takes a data grid containing the load, temperature and occupancy historical data, and returns a data model similar to the one in LBNL-4944E. The most notable difference is that occupancy is modeled using a 0 or 1 instead of creating a separate model for both modes.
The state
parameter is a dictionary which may contain the following keys for configuring LBNL:
intervals
: The number of temperature intervals to use. (default: 6)minTemp
: The minimum temperature to use for computing temperature
intervals. By default, we find the min temperature of the temp column.
maxTemp
: The maximum temperature to use for computing temperature intervals. By default we find the max temperature of the temp column.Perform LBNL-4944E forecasting using a model trained by lbnlTrain for the given dates. Returns a grid containing the recent power, forecasted power, and occupancy. The opts
parameter currently supports the following options:
backcast
: set this marker tag to do "backcasting"Get a trained model using the methodology outlined in LBNL-4944E.
site
: The site to train the model against. Must have a site meter
with a power sensor point.
dates
: Use historical data from this date range for training the model.The model metadata will include the computed cvrsme
and nmbe
metrics.